In some situations, machine learning (ML) or computer vision (CV) models don’t have vast amounts of data to compare what they’re seeing. Instead, an algorithmically-generated model has one attempt to verify the data they’re presented with; this is known as one-shot learning.
Automated passport scanners, turnstiles, and signature verifications limit what an algorithmic model can compare an object against, such as a single passport photo for one individual. Image detection scanners and the computer vision models behind them have one shot at verifying the data they’re being presented with. Hence the value of one-shot learning.
This glossary post explains one-shot learning in more detail, including how it works and some real-world use cases.
What Is One-Shot Learning in AI?
One-shot learning is a machine learning-based (ML) algorithm that compares the similarities and differences between two images. The goal is simple: verify or reject the data being scanned. In human terms, it’s a computer vision approach for answering one question, is this person who they claim to be?
Unlike traditional computer vision projects, where models are trained on thousands of images or videos or detect objects from one frame to the next, one-short learning algorithms are given limited data to compare images against.
In many ways, one-shot algorithms are simpler than most computer vision models. However, other computer vision and ML models are given access to vast databases to improve accuracy and training outputs in comparison to one-shot or few-shot learning algorithms.
How Does One-Shot Learning Work?
Unlike other, more complex computer vision models, one-shot learning isn’t about classification, identifying objects, or anything more involved.
One-shot learning is a computer vision-driven comparison exercise. Therefore, the problem is simply about verifying or rejecting whether an image (or anything else being presented) matches the same image in a database.
A neural network or computer vision model matches and compares the two data points, generating only one of two answers, yes or no.
Generating that answer involves using a version of convolutional neural networks (CNNs) known as Siamese Neural Networks (SNNs). Training these models involves a verification and generalization stage so that the algorithms can make positive or negative verifications in real-world use cases in seconds.
One-Shot vs. Few-Shot Learning
Few-shot learning is similar in almost every way. The only difference is that a computer vision model has a few more data points to compare an object against. Instead of one image, the database might have three or four.
Few-shot learning works the same way as one-shot. It still operates as a comparison-based approach, except the CV model making the comparison has access to slightly more data.
Examples of One-Shot and Few-Shot Learning in Computer Vision
One-shot and few-shot computer vision algorithms and models are mainly used in image classification, object detection and comparison, localization, and speech recognition.
Real-world use cases involve face and signature recognition and verification. Such as airport passport scanners or law enforcement surveillance cameras, scanning for potential terrorists in crowded public places.
Banks and other institutions use this approach to verify ID against the copy stored in their databases. As do hotels, to ensure keycards only open the door to the assigned room.
One-shot and few-shot learning is also used for signature verification. Plus, one-shot learning is integrated into drone and self-driving car technology, helping more complex algorithms detect objects in the environment. It’s also helpful when using neural networks during automated translations.
Accelerate Machine Learning for Optimal, Accurate AI Models
For one-shot and few-shot computer vision projects, you need machine learning software to optimize and ensure accuracy for the AI-based (artificial intelligence) models you’re deploying in the field.
When you train a one-shot algorithm on a wider range of data, the results will be more accurate. One solution for this is Encord and Encord Active.
Encord improves the efficiency of labeling data, making AI models more accurate. Encord Active is an open-source active learning framework for computer vision: a test suite for your labels, data, and models.
Having the right tools is a valuable asset for organizations that need CV models to get it right the first time.
Encord is a comprehensive AI-assisted platform for collaboratively annotating data, orchestrating active learning pipelines, fixing dataset errors, and diagnosing model errors & biases. Try it for free today.
What’s next?
“I want to start annotating” — Get a free trial of Encord Annotate here.
“I want to get started right away” — You can find Encord Active on Github here or try the quickstart Python command from our documentation.
“Can you show me an example first?” — Check out this Colab Notebook.
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Originally published at https://encord.com.
